Estimating Intrinsic Camera Parameters from the Fundamental Matrix Using an Evolutionary Approach

  • Anthony Whitehead1Email author and

    Affiliated with

    • Gerhard Roth2

      Affiliated with

      EURASIP Journal on Advances in Signal Processing20042004:412751

      DOI: 10.1155/S1110865704401024

      Received: 30 June 2002

      Published: 8 July 2004

      Abstract

      Calibration is the process of computing the intrinsic (internal) camera parameters from a series of images. Normally calibration is done by placing predefined targets in the scene or by having special camera motions, such as rotations. If these two restrictions do not hold, then this calibration process is called autocalibration because it is done automatically, without user intervention. Using autocalibration, it is possible to create 3D reconstructions from a sequence of uncalibrated images without having to rely on a formal camera calibration process. The fundamental matrix describes the epipolar geometry between a pair of images, and it can be calculated directly from 2D image correspondences. We show that autocalibration from a set of fundamental matrices can simply be transformed into a global minimization problem utilizing a cost function. We use a stochastic optimization approach taken from the field of evolutionary computing to solve this problem. A number of experiments are performed on published and standardized data sets that show the effectiveness of the approach. The basic assumption of this method is that the internal (intrinsic) camera parameters remain constant throughout the image sequence, that is, the images are taken from the same camera without varying such quantities as the focal length. We show that for the autocalibration of the focal length and aspect ratio, the evolutionary method achieves results comparable to published methods but is simpler to implement and is efficient enough to handle larger image sequences.

      Keywords and phrases

      autocalibration dynamic hill climbing fundamental matrix evolutionary computing epipolar geometry 3D reconstruction

      Authors’ Affiliations

      (1)
      School of Computer Science, Carleton University
      (2)
      National Research Council of Canada

      Copyright

      © Whitehead and Roth 2004